A Machine Learning Approach to Predict an Early Biochemical Recurrence after a Radical Prostatectomy

Park, Seongkeun; Byun, Jieun; Woo, Ji Young

doi:10.3390/app10113854

Cited by 2 publications

(2 citation statements)

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“…Second, the relatively small sample size may have affected the predictive value. Like our results, a previous study in patients with prostate cancer evaluated the performance of DNN, k‐nearest neighborhood, and DT algorithms to identify early recurrence and reported AUCs of 0.607, 0.596, and 0.534, respectively 13 . They discussed that the small numbers of study participants might have impacted the poor performance of their ML models.…”

Section: Discussionsupporting

confidence: 68%

“…Like our results, a previous study in patients with prostate cancer evaluated the performance of DNN, k‐nearest neighborhood, and DT algorithms to identify early recurrence and reported AUCs of 0.607, 0.596, and 0.534, respectively. 13 They discussed that the small numbers of study participants might have impacted the poor performance of their ML models. The accuracy of ML algorithms depends on the input size, that is, it can be increased with an increase in the data input.…”

Section: Discussionmentioning

confidence: 99%

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Application of several machine learning algorithms for the prediction of afatinib treatment outcome in advanced‐stage EGFR‐mutated non‐small‐cell lung cancer

2022

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Background: The present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1-year afatinib continuation and 2-year survival after afatinib initiation and to identify the differences in survival outcomes between ML-classified strata. Methods: Data that were also used in the RESET study were retrospectively collected from 16 hospitals in South Korea. A stratified random sampling method was applied to split the data into training and test sets (70:30 split ratio). Clinical information, such as age, sex, tumor stage, smoking, performance status, metastasis, type of metastasis, dose adjustment, and pathologic information on EGFR mutations were inputted. Training was performed using eight ML algorithms: logistic regression, decision tree, deep neural network, random forest, support vector machine, boosting, bagging, and the naïve Bayes classifier. The model performance was assessed based on sensitivity, specificity, and accuracy. Area under the receiver operator characteristic curve (AUC) was calculated and compared between the ML models using DeLong's test. A Kaplan-Meier (KM) curve was used to visualize the identified strata obtained from the ML models. Results: No significant differences in the input variables were observed between the training and test datasets. The best-performing models were support vector machine in predicting 1-year afatinib continuation (AUC 0.626) and decision tree in 2-year survival after afatinib start (AUC 0.644), although the performances of the ML models were comparable and did not display any predictive roles. KM analysis and log-rank test revealed significant differences between the strata identified from the ML model (p < 0.001) in terms of both time-on-treatment (TOT) and overall survival (OS). Conclusion:The performances of ML models in our study found no discernible roles in predicting afatinib-related outcomes, although the identified strata revealed different TOT and OS in the KM analysis. This implies the strength of ML in predicting the survival outcome, as well as the limitation of electronic medical record-based variables in ML algorithms. Careful consideration of variable inclusion is likely to improve the general model performance. K E Y W O R D S machine learning, NSCLC, outcome, survival BACKGROUND Lung cancer is one of the leading causes of death, accounting for approximately 23.3% of all cancer-related deaths in

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Section: Discussionsupporting

confidence: 68%

Section: Discussionmentioning

confidence: 99%

Application of several machine learning algorithms for the prediction of afatinib treatment outcome in advanced‐stage EGFR‐mutated non‐small‐cell lung cancer

2022

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Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review

Liu,

Zhang,

Woon

et al. 2024

Cancers

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Background/Objectives: Biochemical recurrence (BCR) after radical prostatectomy (RP) is a significant predictor of distal metastases and mortality in prostate cancer (PCa) patients. This systematic review aims to evaluate the accuracy of artificial intelligence (AI) in predicting BCR post-RP. Methods: Adhering to PRISMA guidelines, a comprehensive literature search was conducted across Medline, Embase, Web of Science, and IEEE Xplore. Studies were included if they utilised AI to predict BCR in patients post-RP. Studies involving patients who underwent radiotherapy or salvage RP were excluded. This systematic review was registered on PROSPERO (International prospective register of systematic reviews) under the ID CRD42023482392. Results: After screening 9764 articles, 24 met the inclusion criteria. The included studies involved 27,216 patients, of whom 7267 developed BCR. AI algorithms developed using radiological parameters demonstrated higher predictive accuracy (median AUROC of 0.90) compared to algorithms based solely on pathological variables (median AUROC of 0.74) or clinicopathological variables (median AUROC of 0.81). According to the Prediction Model Risk of Bias Assessment Tool (PROBAST), the overall risk of bias was unclear in three studies due to ambiguous inclusion criteria and the exclusion of many patients because of missing follow-up data. In seven studies, the developed AI outperformed or was at least equivocal to traditional methods of BCR prediction. Conclusions: AI shows promise in predicting BCR post-RP, particularly when radiological data were used in its development. However, the significant variability in AI performance and study methodologies highlights the need for larger, standardised prospective studies with external validation prior to clinical application.

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A Machine Learning Approach to Predict an Early Biochemical Recurrence after a Radical Prostatectomy

Cited by 2 publications

References 37 publications

Application of several machine learning algorithms for the prediction of afatinib treatment outcome in advanced‐stage EGFR‐mutated non‐small‐cell lung cancer

Application of several machine learning algorithms for the prediction of afatinib treatment outcome in advanced‐stage EGFR‐mutated non‐small‐cell lung cancer

Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review

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